---

name: file-converter description: This skill handles file format conversions across documents (PDF, DOCX, Markdown, HTML, TXT), data files (JSON, CSV, YAML, XML, TOML), and images (PNG, JPG, WebP, SVG, GIF). Use when the user requests converting, transforming, or exporting files between formats. Generates conversion code dynamically based on the specific request. author: Joseph OBrien status: unpublished updated: '2025-12-23' version: 1.0.1 tag: skill type: skill

File Converter

Overview

Convert files between formats across three categories: documents, data files, and images. Generate Python code dynamically for each conversion request, selecting appropriate libraries and handling edge cases.

Conversion Categories

Documents

From	To	Recommended Library
Markdown	HTML	markdown or mistune
HTML	Markdown	markdownify or html2text
HTML	PDF	weasyprint or pdfkit (requires wkhtmltopdf)
PDF	Text	pypdf or pdfplumber
DOCX	Markdown	mammoth
DOCX	PDF	docx2pdf (Windows/macOS) or LibreOffice CLI
Markdown	PDF	Convert via HTML first, then to PDF

Data Files

From	To	Recommended Library
JSON	YAML	pyyaml
YAML	JSON	pyyaml
JSON	CSV	pandas or stdlib csv + json
CSV	JSON	pandas or stdlib csv + json
JSON	TOML	tomli/tomllib (read) + tomli-w (write)
XML	JSON	xmltodict
JSON	XML	dicttoxml or xmltodict.unparse

Images

From	To	Recommended Library
PNG/JPG/WebP/GIF	Any raster	Pillow (PIL)
SVG	PNG/JPG	cairosvg or svglib + reportlab
PNG	SVG	potrace (CLI) for tracing, limited fidelity

Workflow

1. Identify source format (from file extension or user statement)
2. Identify target format
3. Check references/ for format-specific guidance
4. Generate conversion code using recommended library
5. Handle edge cases (encoding, transparency, nested structures)
6. Execute conversion and report results

Quick Patterns

Data: JSON to YAML

import json
import yaml

with open("input.json") as f:
    data = json.load(f)

with open("output.yaml", "w") as f:
    yaml.dump(data, f, default_flow_style=False, allow_unicode=True)

Data: CSV to JSON

import csv
import json

with open("input.csv") as f:
    reader = csv.DictReader(f)
    data = list(reader)

with open("output.json", "w") as f:
    json.dump(data, f, indent=2)

Document: Markdown to HTML

import markdown

with open("input.md") as f:
    md_content = f.read()

html = markdown.markdown(md_content, extensions=["tables", "fenced_code"])

with open("output.html", "w") as f:
    f.write(html)

Image: PNG to WebP

from PIL import Image

img = Image.open("input.png")
img.save("output.webp", "WEBP", quality=85)

Image: SVG to PNG

import cairosvg

cairosvg.svg2png(url="input.svg", write_to="output.png", scale=2)

Resources

Detailed guidance for complex conversions is in references/:

• references/document-conversions.md - PDF handling, encoding issues, styling preservation
• references/data-conversions.md - Schema handling, type coercion, nested structures
• references/image-conversions.md - Quality settings, transparency, color profiles

Consult these references when handling edge cases or when the user has specific quality/fidelity requirements.